Association between the cardiometabolic index and sleep health in the United States: A cross-sectional study

Association between the cardiometabolic index and sleep health in the United States: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between the cardiometabolic index and sleep health in the United States: A cross-sectional study Chenpeng Zheng, Chaote Zhao, Ran Zhang, Xiong Lei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7079101/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background: The cardiometabolic index (CMI) is a novel indicator of central obesity. This study aimed to investigate the association between CMI and sleep health. Methods: Using data from the National Health and Nutrition Examination Survey (NHANES), we calculated CMI values and employed univariate and multivariate logistic regression analyses to determine whether CMI is an independent risk factor for sleep health. CMI was categorized into quartiles (Q1 to Q4). Subgroup analyses were conducted, and interaction P-values were calculated to identify high-risk populations. Results: A total of 6,600 participants were included in the study. The prevalence of sleep disturbances was 22.2% (n = 1,589), and 7.6% (n = 504) of participants reported sleep disorders. Higher CMI levels were significantly associated with poor sleep health. Specifically, CMI was independently associated with an increased risk of sleep disturbances (OR: 1.35; 95% CI: 1.19-1.54) and sleep disorders (OR: 1.22; 95% CI: 1.0002-1.50). Compared to the Q1 group, the risk of sleep disturbances increased by 49% in the Q4 group. Subgroup analyses revealed statistically significant interactions between CMI and sleep disturbances or sleep disorders in males and individuals with hypertension (all P for interaction < 0.05). These findings highlight the need for increased attention to this association, particularly among males and hypertensive populations. Conclusion: The findings suggested that CMI might be independently associated with sleep health, particularly sleep disturbances. Interventions targeting CMI could potentially improve sleep health outcomes. “Level of Evidence: Level II, controlled trial without randomization” Cardiometabolic index Sleep health NHANES Obesity Cross-sectional studies Figures Figure 1 Figure 2 1. Introduction Sleep disorder as a public health issue has been a major concern in the United States. And the prevalence of sleep disorders has been increasing every year ( 1 ). Many diseases have been linked to sleep disorders. Sleep disorders not only increase mental problems and social costs ( 2 ), but are also a risk factor for chronic diseases such as hypertension and diabetes ( 3 ). Therefore, there is an urgent need to identify people with high-risk factors for sleep disorders and intervene earlier to save social care costs. There is a significant relationship between blood lipid levels and body measurements with sleep health. In a study of poor sleep health in an elderly Iranian population and lipid concentrations, poor sleep health is positively associated with triglyceride (TG) and negatively associated with high-density lipoprotein cholesterol (HDL-C) ( 4 ). A study found that after the health management of volunteers at high risk of chronic diseases, a decrease in waist circumference (WC) and TG is found compared to the control group, accompanied by a reduction of the insomnia severity index ( 5 ).However, there is also evidence of a possible association between prolonged sleep and elevated triglyceride levels ( 6 ). Better sleep health is strongly associated with lower WC ( 7 ). However, no studies have been conducted on sleep health in conjunction with lipid levels and body measurements. With the advancement of technology, many devices or laboratory methods are used to predict sleep problems. Individual sleep health problems assessed with polysomnography often do not correlate with self-reported sleep health problems, and the accuracy of assessing sleep health is a challenge ( 8 ). Recently, there have been some efforts to predict sleep problems by looking for metabolomic biomarkers and machine-learning methods ( 9 ). Sleep apnea is usually detected using polysomnography, but there are limitations in terms of equipment cost and detection time. Detection systems have also been embedded in modern wearable devices but do not apply to the majority of the population ( 10 ). Therefore, improving sleep health problem is urgent in society. Cardiometabolic index (CMI) was a simple index, a concept originally developed to distinguish diabetes from hyperglycemia, determined by obesity and lipid levels ( 11 ). In subsequent studies, CMI is associated with vascular-related diseases such as atherosclerosis ( 12 ), erectile dysfunction ( 13 ), and ischemic stroke ( 14 ). CMI could effectively identify obstructive sleep apnea and has strong application value ( 15 ). More recently, the value of CMI has been amplified and shown to be associated with depression ( 16 ). This suggested that CMI was also strongly associated with psychiatric disorders. Exploring the relationship between CMI and sleep health seemed feasible. The purpose of this study was to explore the relationship between CMI and sleep health in the US population. Attempts were made to provide early interventions for those who might have sleep health problem. 2. Materials and methods 2.1 Study population The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional survey database in the United States that holds valuable information on the health and nutritional status of U.S. adults, such as demographics, laboratory tests, physical examinations, and questionnaires. These data can be used to explore risk factors for disease and to shape public policy and are found on the official website ( https://www.cdc.gov/nchs/nhanes/ ). We selected a study population from five National Health and Nutrition Examination Survey (NHANES) cycles (2005–2014) that included data on both CMI components and sleep health. A total of 32,096 participants with complete sleep health data were included. First, missing data on body mass index (BMI), height (HT), and WC (2,912) were excluded from the study. Next, we excluded missing data on TG, low-density lipoprotein cholesterol (LDL-C), and HDL-C (15,954) in this population. Then, we excluded other missing data including hypertension ( 16 ), caffeine intake (567), alcohol consumption (5,369), cotinine ( 8 ), poverty-to-income ratio (PIR) (488), educational background ( 3 ), and pregnant (135). Finally, participants had missing data on albumin, alanine aminotransferase, aspartate aminotransferase, uric acid, and creatinine ( 44 ). The final sample size of the study was 6,600 (Fig. 1 ). 2.2 Exposure definitions CMI was designated as the exposure variable and calculated using the following formula: CMI = TG (mmol/L) / HDL-C (mmol/L) * [WC (cm) / HT (cm)] ( 11 ). Prior to data collection, HT (standing height) and WC measurements were standardized through a two-day training program. This training included viewing instructional videos, understanding measurement protocols, and demonstrating the techniques on volunteers under the supervision of a reviewer. TC and HDL-C levels were analyzed within 48 hours of sample collection. Incomplete samples were frozen and thawed only once to ensure data integrity. TC levels were measured using the Beckman UniCel® DxC800 Synchron analyzer, while HDL-C levels were assessed using the Roche/Hitachi Modular P Chemistry Analyzer. All measurements were conducted under the supervision of reviewers and adhered to the anthropometric procedure manual as well as the guidelines set by the Centers for Disease Control and Prevention (CDC). Next, the participants were then divided into quartiles according to their CMI values. The Quartile 1(Q1) was 0.84. 2.3 Sleep quality measures and outcome definitions Sleep health, the primary endpoint of this study, was assessed using a three-dimensional questionnaire ( 17 ). Sleep disturbances were evaluated based on the following question: “Have you ever told a doctor or other health professional that you have trouble sleeping?”. Sleep disorders were identified using the question: “Have you ever been told by a doctor or other health professional that you have a sleep disorder?”. Sleep duration was determined by the question: “How much sleep do you get (hours)?”. 2.4 Covariate definitions After reviewing several studies on the relationship between CMI and sleep health ( 18 – 21 ), the following confounding factors were included in our analysis. Age was 18 to 85. Gender was categorized by male and female participants. Non-Hispanic white, non-Hispanic black, other Hispanic, Mexican-American, and other races/ethnicities were the categories of race/ethnicity. Below high school, high school, and above high school were the categories of educational background. PIR was used to defined the income level of the population. Smoking status was assessed by serum cotinine levels. The number of alcoholic beverages consumed in the last 12 months was used to measure the level of alcohol consumption. BMI was categorized as 30. Caffeine intake was 24 hours prior to the interview and was averaged over two 24-hour dietary recall interviews (Day 1 and Day 3 to Day 10 post). Participants were asked “Ever told you had high blood pressure” to define hypertension. In addition, LDL-C (mmol/L), albumin (g/L), alanine aminotransferase (U/L), aspartate aminotransferase (U/L), uric acid (mg/dL), and creatinine (µmol/L) also included in covariates. 2.5 Statistical analysis During the data screening process, data showing “missing”, “rejected” or “don‘t known” would be excluded from our study. The final population included in the study had complete data. We analyzed the data after weighted. The continuous variables were tested for normal distribution and the results were all non-normal continuous variables. Subsequently, this study expressed non-normal continuous variables by median (IQR) and categorical variables by weighted percentages. The Rao-Scott chi-squared test and Kruskal-Wallis test were used to investigate the characteristics of differences in categorical and non-normal continuous variables in the CMI quartile (Q1 to Q4), respectively. Weighted logistic regression models and weighted linear regression models were used to investigate the relationship between CMI and sleep health in the three models and P values were tested. The Model 1 was a crude model. The Model 2 was only adjusted for gender and age. The Model 3 was adjusted for all covariates (age, gender, race/ethnicity, educational background, PIR, serum cotinine, alcohol consumption, BMI, caffeine intake, hypertension, LDL-C, albumin, alanine aminotransferase, aspartate aminotransferase, uric acid, and creatinine). The relationship between CMI with sleep disorder and sleep trouble was expressed as odds ratios (OR) and 95% confidence intervals (95% CI), while the relationship between CMI and sleep duration was by the mean difference (MD) and 95% CI. In addition, Q1 to Q4 were used as categorical variables for trend analysis (P for trend). Finally, subgroup analyses by gender, age, education background, and hypertension were performed to screen for key populations, and the P for interaction was also calculated. The R (4.2.2) software was used to analyze all statistics. All tests were two-sided and a P value less than 0.05 was considered statistically different. 3. Results 3.1 Baseline characteristics Between 2005 and 2014 (5 survey cycles), the number of participants who fulfilled the requirements of the study was 6,600, of whom 3,609 were men and 2,991 were women, with an average age of 45 years. Of these, 1,589 (22.2%) reported sleep trouble and 504 (7.6%) reported sleep disorder. From Q1 to Q4 of CMI, Non-Hispanic White, educational background above high school, and BMI ˃30 were more common in the Q4 population. Compared to the Q1 population, the Q4 population has a higher serum cotinine, higher caffeine intake, higher alanine aminotransferase, higher aspartate aminotransferase, uric acid, creatinine, and higher LDL-cholesterol, while having lower PIR (Table 1 ). Table 1 Baseline characteristics of participants according to the CMI's quartile. ALL Q1 (< 0.28) Q2 (0.28–0.48) Q3 (0.48–0.84) Q4 (˃0.84) P value N = 6,600 N = 1,609 N = 1,639 N = 1,657 N = 1,695 Age (year) 45 [32; 57] 40 [28; 53] 40 [30; 57] 46 [32; 58] 47 [37; 59] < 0.001 Gender < 0.001 Male 3,609 (52.9%) 657 (38.3%) 874 (51.3%) 951 (55.6%) 1,127 (66.1%) Female 2,991 (47.1%) 952 (61.7%) 765 (48.7%) 706 (44.4%) 568 (33.9%) Race/Ethnicity < 0.001 Mexican American 967 (7.4%) 148 (5.1%) 206 (6.2%) 276 (8.3%) 337 (9.9%) Other Hispanic 536 (4.3%) 101 (3.8%) 127 (3.8%) 154 (5.3%) 154 (4.5%) Non-Hispanic White 3,341 (74%) 771 (71.5%) 866 (76.9%) 808 (72.3%) 896 (75.2%) Non-Hispanic Black 1,259 (9.3%) 437 (13.6%) 323 (8.9%) 293 (8.7%) 206 (6.0%) Other race 497 (5.0%) 152 (6.0%) 117 (4.2%) 126 (5.4%) 102 (4.4%) Educational background < 0.001 Highschool 3,793 (65.7%) 1,064 (73.3%) 972 (66.8%) 911 (64.2%) 846 (58.6%) Highschool/ general educational development 1,477 (20.8%) 308 (17.4%) 355 (20.0%) 397 (21.2%) 417 (24.7%) Body mass index < 0.001 30 2,210 (32.3%) 168 (8.4%) 397 (21.9%) 669 (39.2%) 976 (58.9%) Sleep trouble < 0.001 No 5,011 (74.8%) 1,281 (76.4%) 1,267 (74.2%) 1,250 (75.4%) 1,213 (70.5%) Yes 1,589 (22.2%) 328 (23.6%) 372 (25.8%) 407 (24.6%) 482 (29.5%) Sleep disorder 0.001 No 6,096 (92.4%) 1,537 (95.1%) 1,529 (93.1%) 1,525 (92.3%) 1,505 (89.1%) Yes 504 (7.6%) 72 (4.9%) 110 (6.9%) 132 (7.7%) 190 (10.9%) Sleep duration (hour) 7.00 [6.00; 8.00] 7.00 [6.00; 8.00] 7.00 [6.00; 8.00] 7.00 [6.00; 8.00] 7.00 [6.00; 8.00] 0.301 Hypertension < 0.001 No 4,529 (70.7%) 1,290 (83.2%) 1,162 (73.6%) 1,089 (68.3%) 988 (58.1%) Yes 2,071 (29.3%) 319 (16.8%) 477 (26.4%) 568 (31.7%) 707 (41.9%) Serum cotinine ((ng/mL)) 0.05 [0.02; 34.09] 0.04 [0.01; 3.39] 0.05 [0.01; 30.16] 0.05 [0.02; 62.13] 0.07 [0.02; 65.90] < 0.001 Poverty-to-income ratio 2.40 [1.75; 5.00] 3.69 [1.94; 5.00] 3.49 [1.83; 5.00] 3.25 [1.64; 5.00] 3.15 [1.57; 5.00] < 0.001 Caffeine intake (mg) 125.50 [46.0; 242.50] 118.26 [33.94; 233.00] 123.03 [49.00; 254.82] 129.0 [48.00; 242.50] 131.47 [52.23; 247.50] < 0.001 Albumin (g/L) 43.00 [41.00; 45.00] 43.00 [41.00; 45.00] 43.00 [41.00; 45.00] 43.00 [41.00; 45.00] 42.00 [40.00; 45.00] < 0.001 Alanine aminotransferase (U/L) 21.00 [17.00; 29.00] 19.0 [15.0; 23.0] 20.0 [16.00; 26.00] 22.0 [18.00; 30.00] 27.0 [20.00; 36.00] < 0.001 Aspartate aminotransferase (U/L) 23.00 [20.00; 27.00] 22.00 [19.00; 26.00] 2.00 [19.00; 26.00] 23.0 [19.00; 28.00] 25.0 [21.00; 29.00] < 0.001 Uric acid (mg/dL) 5.50 [4.60; 6.40] 4.70 [4.00; 5.60] 5.30 [4.50; 6.10] 5.60 [4.80; 6.50] 6.20 [5.40; 7.00] < 0.001 Creatinine (umol/L) 76.02 [65.42; 88.40] 72.49 [62.76; 83.10] 76.02 [65.42; 88.40] 76.91 [67.18; 88.40] 79.56 [68.95; 90.17] < 0.001 Alcohol consumption (drinks) 2.00 [1.00; 3.00] 2.00 [1.00; 3.00] 2.00 [1.00; 3.00] 2.00 [1.00; 3.00] 2.00 [1.00; 3.00] < 0.001 LDL-cholesterol (mmol/L) 2.92 [2.35; 3.52] 2.59 [2.12; 3.13] 2.90 [2.38; 3.47] 3.08 [2.53; 3.70] 3.08 [2.43; 3.75] < 0.001 3.2 Association between the CMI and sleep quality The logistic regression model for the relationship between CMI and sleep health was shown in Table 2 . The CMI was statistically positively associated with sleep trouble in all models and all the P for trend was less than 0.05. Compared to the Q1 group, the risk of sleep trouble increased in the Q2, Q3, and Q4 groups by 7%, 22%, and 49 in the Model 3, respectively. The CMI was positively associated with sleep disorder (P less than 0.05). However, there was no significant difference according to categorical variables from the Q1 group to the Q4 group in Model 3. In addition, CMI showed a negative association with sleep duration in Model 1 and Model 2, but there was no relationship between CMI and sleep duration in Model 3. Table 2 The relationship between CMI and sleep quality Model 1 Model 2 Model 3 Sleep trouble OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value Continue 1.35 (1.21 to 1.50) < 0.0001 1.43 (1.29 to 1.60) < 0.0001 1.35 (1.19 to 1.54) < 0.0001 Q1 Reference Reference Reference Q2 1.10 (0.90 to 1.34) 0.3462 1.15 (0.93 to 1.41) 0.1869 1.07 (0.85 to 1.35) 0.5427 Q3 1.24 (1.00 to 1.53) 0.0485 1.30 (1.05 to 1.61) 0.0156 1.22 (0.94 to 1.59) 0.1349 Q4 1.50 (1.22 to 1.83) 0.0002 1.64 (1.33 to 2.01) < 0.0001 1.49 (1.14 to 1.95) 0.0042 P for trend < 0.0001 < 0.0001 0.0028 Sleep disorder Continue 1.72 (1.49 to 1.99) < 0.0001 1.66 (1.42 to 1.94) < 0.0001 1.22 (1.0002 to 1.50) 0.0498 Q1 Reference Reference Reference Q2 1.43 (0.96 to 2.11) 0.0775 1.35 (0.91 to 2.00) 0.1395 1.08 (0.72 to 1.61) 0.7190 Q3 1.61 (1.05 to 2.48) 0.0305 1.48 (0.95 to 2.31) 0.0786 0.96 (0.58 to 1.57) 0.8677 Q4 2.37 (1.63 to 3.44) < 0.0001 2.11 (1.44 to 3.09) 0.0002 1.07 (0.70 to 1.63) 0.7408 P for trend < 0.0001 0.0001 0.8547 Sleep duration MD (95% CI) P value MD (95% CI) P value MD (95% CI) P value Continue -0.07 (-0.14 to 0.01) 0.0736 -0.06 (-0.14 to 0.01) 0.0936 -0.0003 (-0.11 to 0.11) 0.9274 Q1 Reference Reference Reference Q2 -0.08 (-0.18 to 0.02) 0.1299 -0.08 (-0.18 to 0.02) 0.1333 -0.04 (-0.14 to 0.07) 0.4739 Q3 -0.13 (-0.24 to -0.03) 0.0139 -0.14 (-0.24 to -0.03) 0.0146 -0.04 (-0.16 to 0.08) 0.5343 Q4 -0.12 (-0.23 to -0.01) 0.0336 -0.12 (-0.24 to -0.01) 0.0344 -0.0003 (-0.11,0.11) 0.9965 P for trend 0.0213 0.0231 0.9692 OR, odds ratio; MD, mean difference; CI, confidence intervals. Q1: the lowest CMI group. Q2: the lower CMI group. Q3: the higher CMI group. Q4: the highest CMI group. The model 1 was not adjusted for any covariates. The model 2 was adjusted by age and gender. The model 3 was adjusted by age, gender, race/ethnicity, educational background, PIR, serum cotinine, alcohol consumption, BMI, Caffeine intake, hypertension, LDL-cholesterol, albumin, alanine aminotransferase, aspartate aminotransferase, uric acid, and creatinine. 3.3 Subgroup analyses In subgroup analyses of CMI with sleep trouble and sleep disorder (Fig. 2 ), statistically significant interactions were marked between CMI and sleep trouble in the gender and hypertension subgroup (P for interaction was 0.0005 and 0.0003, respectively). Similar results were found in CMI and sleep disorder (P for interaction was 0.0236 and 0.0049, respectively). No significant interactions were observed in the subgroups stratified by educational background, age, or race/ethnicity. However, a positive association between CMI and sleep trouble was identified in non-Hispanic White and non-Hispanic Black populations. 3.4 Additional analysis To explore the associations between CMI parameters and sleep health outcomes, we analyzed the relationships with sleep trouble, sleep disorders, and sleep duration independently (Table 3 ). The TG was positively associated with sleep trouble (OR,1.23; 95%CI, 1.12 to 1.35). In addition, WC was significantly positively associated with sleep trouble (OR,1.02; 95%CI, 1.01 to 1.03) and sleep disorder (OR,1.03; 95%CI, 1.02 to 1.04). However, no association was found between HDL-C or height and sleep health. Table 3 Correlation of sleep quality with parameters in CMI Sleep trouble (OR, 95%CI) P value Sleep disorder (OR, 95%CI) P value Sleep duration (MD, 95%CI) P value TG 1.23 (1.12 to 1.35) < 0.001 1.08 (0.90 to 1.29) 0.4106 0.02 (-0.04 to 0.08) 0.5112 HDL-C 0.83 (0.65 to 1.07) 0.1487 0.95 (0.68 to 1.33) 0.7715 0.07 (-0.04 to 0.19) 0.1920 WC 1.02 (1.01 to 1.03) < 0.001 1.03 (1.02 to 1.04) < 0.001 -0.0001 (-0.0042 to 0.004) 0.9618 Height 1.01(1.00 to 1.03) 0.1055 1.00 (0.98 to 1.03) 0.7455 0.0043 (-0.0004 to 0.01) 0.0719 TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; WC, waist circumference; OR, odds ratio; CI, confidence intervals; MD, mean difference. The model was adjusted by age, gender, race/ethnicity, educational background, PIR, serum cotinine, alcohol consumption, BMI, caffeine intake, hypertension, LDL-cholesterol, albumin, alanine aminotransferase, aspartate aminotransferase, uric acid, and creatinine. 4. Discussion A population-based cohort study revealed a significant association between CMI levels and sleep health, particularly in relation to sleep trouble. Even after adjusting for potential confounding variables, including gender, age, education level, coffee consumption, and laboratory test results, CMI remained positively correlated with sleep trouble. This association was especially pronounced in male and hypertensive populations. The CMI was considered an indicator of central obesity. Previous studies found that excessive daytime sleepiness (EDS) in pilots leads to an association with reduced performance and fatigue, centrally obese (based on WC) pilots have a high incidence of EDS ( 22 ). Studies on the relationship between sleep duration and different obesity indicators showed a negative association between sleep duration and obesity-related indicators (BMI, waist circumference, etc.) ( 23 ). School-aged children showed that later bedtimes on non-school days may be associated with increased Waist Height Ratio ( 24 ). A study of the sleep status of Saudi adults showed that higher TG levels were associated with poorer sleep health ( 25 ). However, few studies combined waist-to-height ratio with lipid indices to reveal the relationship between CMI and sleep health. Our study demonstrated that CMI was positively associated with sleep trouble as well as sleep disorder, however, there were no significant differences between CMI with sleep duration in the adjusted model. Similar to previous studies that reported no relationship between obesity and sleep duration ( 26 , 27 ). CMI as an indicator involving obesity, our results were similar to previous studies. Although the underlying molecular mechanism of action between CMI and sleep health is not clear, some explanations could be derived from previous relevant studies. A larger CMI means a greater tendency towards centripetal obesity. Previous studies have shown that obesity means higher levels of oxidation and inflammation in the body ( 28 ). Notably, antioxidant status has been positively correlated with sleep quality in U.S. populations ( 18 ). while chronic inflammation, which is prevalent in obese individuals, has been shown to adversely affect sleep health ( 29 ). Furthermore, obesity-induced alterations in gut microbiota composition and function may represent a critical therapeutic target ( 30 ). as interventions targeting gut dysbiosis and intestinal inflammation show promise for improving sleep disorders ( 31 ). These interconnected physiological mechanisms may collectively mediate the association between cardiometabolic index and sleep health outcomes. Subgroup analyses based on gender, age, education level, hypertension status, and race/ethnicity indicated that the association between CMI and sleep trouble, including sleep disorders, varied significantly by gender and hypertension status. CMI would be higher in older men than in younger. Overall, males have a higher CMI than females, while female's CMI is increasing with age ( 32 ). A Japanese study showed that the TG/HDL-C ratio and CMI increased with increasing white blood cell counts in Japanese males ( 33 ). Among Japanese men with diabetes, mild-to-moderate drinking participants had lower CMI than non-drinking participants, possibly due to the positive association between alcohol consumption and HDL-C ( 34 ). In contrast, an analysis of NHANES showed gender differences in blood lipids and leukocytes, with increased HDL-C appearing to correlate negatively with leukocyte counts in males, and lowering blood lipids might have anti-inflammatory and immunomodulatory effects ( 35 ). The effect of sex hormone levels on sleep should not be ignored. Females were worse than males at subjective sleep but performed better than males when tested using polysomnography, sex hormones appeared to operate in the area of the brain that controls sleep ( 36 ). In addition, sleep quality among non-Hispanic Black individuals was poorer compared to Hispanic individuals, potentially due to various social stressors ( 37 ). Blacks have the highest rate of obesity in the U.S. population ( 38 ). perceptions of neighborhood environments may significantly influence sleep health in certain racial groups, such as Black and Hispanic populations ( 39 ). These findings suggest that race-specific sleep interventions may be both feasible and necessary. In subgroup analyses, the relationship between CMI and sleep disorder was statistically significant in both males and females, whereas the relationship between CMI and sleep disorder was only present in males. The diagnosis of sleep disorders was obtained from physicians, and our results were consistent with those of previous studies. Attention should be paid to the performance of males in sleep health. The importance of CMI in hypertensive populations has also been validated in other studies. In hypertensive patients, CMI level is positively associated with the risk of new cardiovascular disease ( 40 ). CMI is shown to correlate with the degree of atherosclerosis ( 12 ). Atherosclerosis is also exacerbated by short sleep and poor sleep health ( 41 ). Alzheimer's disease (AD) is often associated with comorbid hypertension and AD progression is often followed by sleep disorders ( 42 ). A community-based cohort study suggesting a non-linear relationship between sleep quality and risk of hypertension was confirmed ( 43 ). A meta-analysis similarly showed that short objective sleep duration can affect cardiovascular health and is associated with a higher risk of hypertension ( 44 ). Our results showed that the association between CMI and sleep disorder was statistically significant in the hypertensive population. Individuals with a higher CMI might have sleep trouble that are comorbid with hypertension. It suggested that we should pay attention to CMI and sleep health in the hypertensive population from the perspective of prevention of cardiovascular and cerebrovascular complications in our priority population. It was feasible to develop interventions for CMI and sleep health. First, on a psychological level, we should encourage participants with sleep problems that having sleep health was possible. Second, enhancing physical activity to reduce WC was a feasible approach according to the CMI calculation ( 20 ), especially among males diagnosed with hypertension. Third, lowering TG levels with safe medication was also a potential measure. This study has several limitations that should be acknowledged. First, the assessment of sleep health and hypertension relied on subjective questionnaire data rather than objective machine-based measurements, although the modified Pittsburgh Sleep Quality Index provided relatively robust sleep health evaluation. Second, potential fluctuations in covariates, particularly the use of sleep medications, may have influenced our results. Third, the cross-sectional design limits our ability to establish causal relationships between the studied variables. Nevertheless, this study possesses notable strengths. The large sample size enhances the generalizability of findings to the U.S. population. Additionally, we systematically controlled for numerous potential confounding variables and performed comprehensive subgroup analyses to identify key population characteristics. Future prospective studies are warranted to validate the potential utility of CMI as an indicator for population-level sleep health improvement. 5. Conclusion The study revealed a significant association between CMI levels and sleep health indicators, offering novel insights for sleep health enhancement strategies. Targeted interventions focusing on CMI modulation, such as pharmacological treatments or exercise regimens aimed at lipid profile optimization and waist circumference reduction, may be potentially effective for population-level sleep health improvement. Furthermore, comprehensive prospective studies are warranted to elucidate the underlying mechanisms of lipid metabolism's impact on sleep regulation. Declarations Ethics approval and consent to participate The National Center for Health Statistics institutional review board approved the program and all participants in NHANES had written informed consent (Protocol Number: Protocol #2005-06; Protocol #2011-17). The studies were conducted in accordance with the local legislation and institutional requirements. Consent for publication Not applicable. Availability of data and materials The NHANES (https://www.cdc.gov/nchs/nhanes/index.htm) provided the data. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding None. Authors' contributions Chenpeng Zheng: Visualization, Data curation, Software, Writing – original draft, Writing – review & editing. Chaote Zhao: Data curation, Software, Visualization, Writing – original draft. Ran Zhang: Data curation, Software, Writing – original draft. Xiong Lei: Supervision, Methodology, Conceptualization, Software, Formal analysis, Data curation, Writing – review & editing. Acknowledgements We were grateful to all the participants and staff involved in the NHANES program. References Deng MG, Nie JQ, Li YY, Yu X, Zhang ZJ. 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Association between Sleep Duration and Metabolic Disorders among Filipino Immigrant Women: The Filipino Women's Diet and Health Study (FiLWHEL). J Obes Metab Syndr. 2023;32(3):224-35. Jefferson T, Addison C, Sharma M, Payton M, Jenkins BC. Association Between Sleep and Obesity in African Americans in the Jackson Heart Study. J Am Osteopath Assoc. 2019;119(10):656-66. Pierson-Bartel R, Ujma PP. Objective sleep quality predicts subjective sleep ratings. Sci Rep. 2024;14(1):5943. Jeppe K, Ftouni S, Nijagal B, Grant LK, Lockley SW, Rajaratnam SMW, et al. Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach. Sci Adv. 2024;10(10):eadj6834. Hayano J, Adachi M, Sasaki F, Yuda E. Quantitative detection of sleep apnea in adults using inertial measurement unit embedded in wristwatch wearable devices. Sci Rep. 2024;14(1):4050. Wakabayashi I, Daimon T. The "cardiometabolic index" as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clin Chim Acta. 2015;438:274-8. Wakabayashi I, Sotoda Y, Hirooka S, Orita H. Association between cardiometabolic index and atherosclerotic progression in patients with peripheral arterial disease. Clin Chim Acta. 2015;446:231-6. Dursun M, Besiroglu H, Otunctemur A, Ozbek E. Association between cardiometabolic index and erectile dysfunction: A new index for predicting cardiovascular disease. Kaohsiung J Med Sci. 2016;32(12):620-3. Wang H, Chen Y, Guo X, Chang Y, Sun Y. Usefulness of cardiometabolic index for the estimation of ischemic stroke risk among general population in rural China. Postgrad Med. 2017;129(8):834-41. Wang D, Chen Y, Ding Y, Tang Y, Su X, Li S, et al. Application Value of Cardiometabolic Index for the Screening of Obstructive Sleep Apnea with or Without Metabolic Syndrome. Nat Sci Sleep. 2024;16:177-91. Zhou X, Tao XL, Zhang L, Yang QK, Li ZJ, Dai L, et al. Association between cardiometabolic index and depression: National Health and Nutrition Examination Survey (NHANES) 2011-2014. J Affect Disord. 2024;351:939-47. Zhang J, Yu S, Zhao G, Jiang X, Zhu Y, Liu Z. Associations of chronic diarrheal symptoms and inflammatory bowel disease with sleep quality: A secondary analysis of NHANES 2005-2010. Front Neurol. 2022;13:858439. Lei X, Xu Z, Chen W. Association of oxidative balance score with sleep quality: NHANES 2007-2014. J Affect Disord. 2023;339:435-42. Xi WF, Yang AM. Association between cardiometabolic index and controlled attenuation parameter in U.S. adults with NAFLD: findings from NHANES (2017-2020). Lipids Health Dis. 2024;23(1):40. Xue H, Zou Y, Yang Q, Zhang Z, Zhang J, Wei X, et al. The association between different physical activity (PA) patterns and cardiometabolic index (CMI) in US adult population from NHANES (2007-2016). Heliyon. 2024;10(7):e28792. Yan L, Hu X, Wu S, Cui C, Zhao S. Association between the cardiometabolic index and NAFLD and fibrosis. Sci Rep. 2024;14(1):13194. Brahmanti RS, Sampurna B, Ibrahim N, Adi NP, Siagian M, Werdhani RA. Obesity and Its Relation to Excessive Daytime Sleepiness in Civilian Pilots. Aerosp Med Hum Perform. 2023;94(11):815-20. Andersen MM, Laurberg T, Bjerregaard AL, Sandbæk A, Brage S, Vistisen D, et al. The association between sleep duration and detailed measures of obesity: A cross sectional analysis in the ADDITION-PRO study. Obes Sci Pract. 2023;9(3):226-34. Viljakainen H, Engberg E, Dahlström E, Lommi S, Lahti J. Delayed bedtime on non-school days associates with higher weight and waist circumference in children: Cross-sectional and longitudinal analyses with Mendelian randomisation. J Sleep Res. 2023:e13876. Al-Musharaf S, Albedair B, Alfawaz W, Aldhwayan M, Aljuraiban GS. The Relationships between Various Factors and Sleep Status: A Cross-Sectional Study among Healthy Saudi Adults. Nutrients. 2023;15(18). Chen S, Yang L, Yang Y, Shi W, Stults-Kolehmainen M, Yuan Q, et al. Sedentary behavior, physical activity, sleep duration and obesity risk: Mendelian randomization study. PLoS One. 2024;19(3):e0300074. Massoudi M, Pourghassem Gargari B, Asghari Jafarabadi M, Norouzi S. Major dietary patterns and sleep quality in relation to overweight/obesity among school children: A case-control study. Health Promot Perspect. 2023;13(4):330-8. Bosch-Sierra N, Grau-Del Valle C, Hermenejildo J, Hermo-Argibay A, Salazar JD, Garrido M, et al. The Impact of Weight Loss on Inflammation, Oxidative Stress, and Mitochondrial Function in Subjects with Obesity. Antioxidants (Basel). 2024;13(7). Engert LC, Ledderose C, Biniamin C, Birriel P, Buraks O, Chatterton B, et al. Effects of low-dose acetylsalicylic acid on the inflammatory response to experimental sleep restriction in healthy humans. Brain Behav Immun. 2024. Benrahla DE, Mohan S, Trickovic M, Castelli FA, Alloul G, Sobngwi A, et al. An orally active carbon monoxide-releasing molecule enhances beneficial gut microbial species to combat obesity in mice. Redox Biol. 2024;72:103153. Du Y, Chen X, Kajiwara S, Orihara K. Effect of Urolithin A on the Improvement of Circadian Rhythm Dysregulation in Intestinal Barrier Induced by Inflammation. Nutrients. 2024;16(14). Wakabayashi I. Relationship between age and cardiometabolic index in Japanese men and women. Obes Res Clin Pract. 2018;12(4):372-7. Wakabayashi I. Associations between leukocyte count and lipid-related indices: Effect of age and confounding by habits of smoking and alcohol drinking. PLoS One. 2023;18(1):e0281185. Wakabayashi I. Inverse association of light-to-moderate alcohol drinking with cardiometabolic index in men with diabetes mellitus. Diabetes Metab Syndr. 2018;12(6):1013-7. Andersen CJ, Vance TM. Gender Dictates the Relationship between Serum Lipids and Leukocyte Counts in the National Health and Nutrition Examination Survey 1999⁻2004. J Clin Med. 2019;8(3). Dorsey A, de Lecea L, Jennings KJ. Neurobiological and Hormonal Mechanisms Regulating Women's Sleep. Front Neurosci. 2020;14:625397. Troxel WM, Haas A, Dubowitz T, Ghosh-Dastidar B, Butters MA, Gary-Webb TL, et al. Sleep Disturbances, Changes in Sleep, and Cognitive Function in Low-Income African Americans. J Alzheimers Dis. 2022;87(4):1591-601. Napoe GS, Kermah D, Mitchell NS, Norris K. Racial Disparities in Nocturia Persist Regardless of BMI Among American Women. Urogynecology (Phila). 2024. Hokett E, Lao P, Avila-Rieger J, Turney IC, Adkins-Jackson PB, Johnson DA, et al. Interactions among neighborhood conditions, sleep quality, and episodic memory across the adult lifespan. Ethn Health. 2024:1-19. Cai X, Hu J, Wen W, Wang J, Wang M, Liu S, et al. Associations of the Cardiometabolic Index with the Risk of Cardiovascular Disease in Patients with Hypertension and Obstructive Sleep Apnea: Results of a Longitudinal Cohort Study. Oxid Med Cell Longev. 2022;2022:4914791. Fan B, Tang T, Zheng X, Ding H, Guo P, Ma H, et al. Sleep disturbance exacerbates atherosclerosis in type 2 diabetes mellitus. Front Cardiovasc Med. 2023;10:1267539. Maciejewska K, Czarnecka K, Szymański P. A review of the mechanisms underlying selected comorbidities in Alzheimer's disease. Pharmacol Rep. 2021;73(6):1565-81. Chen C, Zhang B, Huang J. Objective sleep characteristics and hypertension: a community-based cohort study. Front Cardiovasc Med. 2024;11:1336613. Dai Y, Vgontzas AN, Chen L, Zheng D, Chen B, Fernandez-Mendoza J, et al. A meta-analysis of the association between insomnia with objective short sleep duration and risk of hypertension. Sleep Med Rev. 2024;75:101914. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 01 Aug, 2025 Editor assigned by journal 12 Jul, 2025 Submission checks completed at journal 10 Jul, 2025 First submitted to journal 08 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7079101","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498910344,"identity":"51ac068d-5df1-49c9-95ce-e44116f0665a","order_by":0,"name":"Chenpeng Zheng","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chenpeng","middleName":"","lastName":"Zheng","suffix":""},{"id":498910345,"identity":"35fcdd93-b7c6-4bd0-b33b-e82915000fba","order_by":1,"name":"Chaote Zhao","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chaote","middleName":"","lastName":"Zhao","suffix":""},{"id":498910346,"identity":"baea0d54-892d-4876-877a-e0897bae4843","order_by":2,"name":"Ran Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Zhang","suffix":""},{"id":498910347,"identity":"6dfa1301-05c5-4fcb-b420-8076bcda0d8c","order_by":3,"name":"Xiong Lei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACPgbGBoYEIIMfwmcmrIUNpkWygXgtUGBwgGgt7M1tEg8q7thtvpGdJsFQYZ3YwH746Aa8WngONhsknHmWvO1G7jYJhjPpiQ08aWk38GqRSGx8kNh2ONnsNlALY9vhxAYJHjP8WuQfNhwAaTGeDdLyjxgtEoxgW+wMpEFaGojRwpMI8svhBIn7bzdbJBxLN24j5Bd+9uPPJH9UHLbn7zm78caHGmvZfvbDx/BqgYHEBhCZwIAUU4SAPbEKR8EoGAWjYAQCABaeS7NCSz/fAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiong","middleName":"","lastName":"Lei","suffix":""}],"badges":[],"createdAt":"2025-07-09 02:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7079101/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7079101/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88948238,"identity":"dfab2aaf-e279-48d0-8ff5-138ace87ee26","added_by":"auto","created_at":"2025-08-13 05:33:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe process of sample screening.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7079101/v1/abfe3d2fbceae93d908dda4f.png"},{"id":88950656,"identity":"6328a42b-61c1-405e-8dfb-a4149042f181","added_by":"auto","created_at":"2025-08-13 05:49:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129638,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analyzed of CMI with sleep trouble and sleep disorder\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup Analysis of the Association Between CMI and sleep trouble, Including Sleep Disorders, Stratified by Gender, Age, Educational Background, Hypertension Status, and Race/Ethnicity. The model was adjusted by age, gender, race/ethnicity, educational background, PIR, serum cotinine, alcohol consumption, BMI, caffeine intake, hypertension, LDL-cholesterol, albumin, alanine aminotransferase, aspartate aminotransferase, uric acid, and creatinine.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7079101/v1/94ad0bf0a6ce44948db2bb87.png"},{"id":88952009,"identity":"8ecbef07-2c5f-461d-bda8-55ec7d9760b3","added_by":"auto","created_at":"2025-08-13 05:57:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1218435,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7079101/v1/345dc047-ac51-45bc-917b-a4c0902a3635.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between the cardiometabolic index and sleep health in the United States: A cross-sectional study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSleep disorder as a public health issue has been a major concern in the United States. And the prevalence of sleep disorders has been increasing every year (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Many diseases have been linked to sleep disorders. Sleep disorders not only increase mental problems and social costs (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), but are also a risk factor for chronic diseases such as hypertension and diabetes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, there is an urgent need to identify people with high-risk factors for sleep disorders and intervene earlier to save social care costs.\u003c/p\u003e\u003cp\u003eThere is a significant relationship between blood lipid levels and body measurements with sleep health. In a study of poor sleep health in an elderly Iranian population and lipid concentrations, poor sleep health is positively associated with triglyceride (TG) and negatively associated with high-density lipoprotein cholesterol (HDL-C) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). A study found that after the health management of volunteers at high risk of chronic diseases, a decrease in waist circumference (WC) and TG is found compared to the control group, accompanied by a reduction of the insomnia severity index (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).However, there is also evidence of a possible association between prolonged sleep and elevated triglyceride levels (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Better sleep health is strongly associated with lower WC (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, no studies have been conducted on sleep health in conjunction with lipid levels and body measurements.\u003c/p\u003e\u003cp\u003eWith the advancement of technology, many devices or laboratory methods are used to predict sleep problems. Individual sleep health problems assessed with polysomnography often do not correlate with self-reported sleep health problems, and the accuracy of assessing sleep health is a challenge (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Recently, there have been some efforts to predict sleep problems by looking for metabolomic biomarkers and machine-learning methods (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Sleep apnea is usually detected using polysomnography, but there are limitations in terms of equipment cost and detection time. Detection systems have also been embedded in modern wearable devices but do not apply to the majority of the population (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Therefore, improving sleep health problem is urgent in society.\u003c/p\u003e\u003cp\u003eCardiometabolic index (CMI) was a simple index, a concept originally developed to distinguish diabetes from hyperglycemia, determined by obesity and lipid levels (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In subsequent studies, CMI is associated with vascular-related diseases such as atherosclerosis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), erectile dysfunction (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and ischemic stroke (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). CMI could effectively identify obstructive sleep apnea and has strong application value (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). More recently, the value of CMI has been amplified and shown to be associated with depression (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This suggested that CMI was also strongly associated with psychiatric disorders. Exploring the relationship between CMI and sleep health seemed feasible.\u003c/p\u003e\u003cp\u003eThe purpose of this study was to explore the relationship between CMI and sleep health in the US population. Attempts were made to provide early interventions for those who might have sleep health problem.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study population\u003c/h2\u003e\u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a cross-sectional survey database in the United States that holds valuable information on the health and nutritional status of U.S. adults, such as demographics, laboratory tests, physical examinations, and questionnaires. These data can be used to explore risk factors for disease and to shape public policy and are found on the official website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe selected a study population from five National Health and Nutrition Examination Survey (NHANES) cycles (2005\u0026ndash;2014) that included data on both CMI components and sleep health. A total of 32,096 participants with complete sleep health data were included. First, missing data on body mass index (BMI), height (HT), and WC (2,912) were excluded from the study. Next, we excluded missing data on TG, low-density lipoprotein cholesterol (LDL-C), and HDL-C (15,954) in this population. Then, we excluded other missing data including hypertension (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), caffeine intake (567), alcohol consumption (5,369), cotinine (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), poverty-to-income ratio (PIR) (488), educational background (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), and pregnant (135). Finally, participants had missing data on albumin, alanine aminotransferase, aspartate aminotransferase, uric acid, and creatinine (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The final sample size of the study was 6,600 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Exposure definitions\u003c/h2\u003e\u003cp\u003eCMI was designated as the exposure variable and calculated using the following formula: CMI\u0026thinsp;=\u0026thinsp;TG (mmol/L) / HDL-C (mmol/L) * [WC (cm) / HT (cm)] (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Prior to data collection, HT (standing height) and WC measurements were standardized through a two-day training program. This training included viewing instructional videos, understanding measurement protocols, and demonstrating the techniques on volunteers under the supervision of a reviewer. TC and HDL-C levels were analyzed within 48 hours of sample collection. Incomplete samples were frozen and thawed only once to ensure data integrity. TC levels were measured using the Beckman UniCel\u0026reg; DxC800 Synchron analyzer, while HDL-C levels were assessed using the Roche/Hitachi Modular P Chemistry Analyzer. All measurements were conducted under the supervision of reviewers and adhered to the anthropometric procedure manual as well as the guidelines set by the Centers for Disease Control and Prevention (CDC). Next, the participants were then divided into quartiles according to their CMI values. The Quartile 1(Q1) was \u0026lt;\u0026thinsp;0.28, the Quartile 2(Q2) was 0.28 to 0.48, the Quartile 3(Q3) was 0.48 to 0.84, and the Quartile 4 (Q4) was \u0026gt;\u0026thinsp;0.84.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Sleep quality measures and outcome definitions\u003c/h2\u003e\u003cp\u003eSleep health, the primary endpoint of this study, was assessed using a three-dimensional questionnaire (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Sleep disturbances were evaluated based on the following question: \u0026ldquo;Have you ever told a doctor or other health professional that you have trouble sleeping?\u0026rdquo;. Sleep disorders were identified using the question: \u0026ldquo;Have you ever been told by a doctor or other health professional that you have a sleep disorder?\u0026rdquo;. Sleep duration was determined by the question: \u0026ldquo;How much sleep do you get (hours)?\u0026rdquo;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Covariate definitions\u003c/h2\u003e\u003cp\u003eAfter reviewing several studies on the relationship between CMI and sleep health (\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), the following confounding factors were included in our analysis. Age was 18 to 85. Gender was categorized by male and female participants. Non-Hispanic white, non-Hispanic black, other Hispanic, Mexican-American, and other races/ethnicities were the categories of race/ethnicity. Below high school, high school, and above high school were the categories of educational background. PIR was used to defined the income level of the population. Smoking status was assessed by serum cotinine levels. The number of alcoholic beverages consumed in the last 12 months was used to measure the level of alcohol consumption. BMI was categorized as \u0026lt;\u0026thinsp;25, 25\u0026ndash;30, and \u0026gt;\u0026thinsp;30. Caffeine intake was 24 hours prior to the interview and was averaged over two 24-hour dietary recall interviews (Day 1 and Day 3 to Day 10 post). Participants were asked \u0026ldquo;Ever told you had high blood pressure\u0026rdquo; to define hypertension. In addition, LDL-C (mmol/L), albumin (g/L), alanine aminotransferase (U/L), aspartate aminotransferase (U/L), uric acid (mg/dL), and creatinine (\u0026micro;mol/L) also included in covariates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eDuring the data screening process, data showing \u0026ldquo;missing\u0026rdquo;, \u0026ldquo;rejected\u0026rdquo; or \u0026ldquo;don\u0026lsquo;t known\u0026rdquo; would be excluded from our study. The final population included in the study had complete data. We analyzed the data after weighted. The continuous variables were tested for normal distribution and the results were all non-normal continuous variables. Subsequently, this study expressed non-normal continuous variables by median (IQR) and categorical variables by weighted percentages.\u003c/p\u003e\u003cp\u003eThe Rao-Scott chi-squared test and Kruskal-Wallis test were used to investigate the characteristics of differences in categorical and non-normal continuous variables in the CMI quartile (Q1 to Q4), respectively. Weighted logistic regression models and weighted linear regression models were used to investigate the relationship between CMI and sleep health in the three models and P values were tested. The Model 1 was a crude model. The Model 2 was only adjusted for gender and age. The Model 3 was adjusted for all covariates (age, gender, race/ethnicity, educational background, PIR, serum cotinine, alcohol consumption, BMI, caffeine intake, hypertension, LDL-C, albumin, alanine aminotransferase, aspartate aminotransferase, uric acid, and creatinine). The relationship between CMI with sleep disorder and sleep trouble was expressed as odds ratios (OR) and 95% confidence intervals (95% CI), while the relationship between CMI and sleep duration was by the mean difference (MD) and 95% CI. In addition, Q1 to Q4 were used as categorical variables for trend analysis (P for trend). Finally, subgroup analyses by gender, age, education background, and hypertension were performed to screen for key populations, and the P for interaction was also calculated.\u003c/p\u003e\u003cp\u003eThe R (4.2.2) software was used to analyze all statistics. All tests were two-sided and a P value less than 0.05 was considered statistically different.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e\n \u003cp\u003eBetween 2005 and 2014 (5 survey cycles), the number of participants who fulfilled the requirements of the study was 6,600, of whom 3,609 were men and 2,991 were women, with an average age of 45 years. Of these, 1,589 (22.2%) reported sleep trouble and 504 (7.6%) reported sleep disorder. From Q1 to Q4 of CMI, Non-Hispanic White, educational background above high school, and BMI ˃30 were more common in the Q4 population. Compared to the Q1 population, the Q4 population has a higher serum cotinine, higher caffeine intake, higher alanine aminotransferase, higher aspartate aminotransferase, uric acid, creatinine, and higher LDL-cholesterol, while having lower PIR (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of participants according to the CMI\u0026apos;s quartile.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1 (\u0026lt;\u0026thinsp;0.28)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2 (0.28\u0026ndash;0.48)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3 (0.48\u0026ndash;0.84)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4 (˃0.84)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;6,600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 [32; 57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 [28; 53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 [30; 57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 [32; 58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 [37; 59]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,609 (52.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e657 (38.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e874 (51.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e951 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,127 (66.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,991 (47.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e952 (61.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e765 (48.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e706 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e568 (33.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRace/Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican\u003c/p\u003e\n \u003cp\u003eAmerican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e967 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e276 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e337 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e536 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic\u003c/p\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,341 (74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e771 (71.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e866 (76.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e808 (72.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e896 (75.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic\u003c/p\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,259 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e437 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e293 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e497 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEducational background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;Highschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,330 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e349 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e432 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;Highschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,793 (65.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,064 (73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e972 (66.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e911 (64.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e846 (58.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighschool/\u003c/p\u003e\n \u003cp\u003egeneral educational development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,477 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e308 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e355 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e397 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e417 (24.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,109 (33.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e984 (64.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e616 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e356 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,281 (34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e457 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e626 (38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e632 (39.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e566 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,210 (32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e397 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e669 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e976 (58.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSleep trouble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,011 (74.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,281 (76.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,267 (74.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,250 (75.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,213 (70.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,589 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e328 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e407 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e482 (29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSleep disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,096 (92.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,537 (95.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,529 (93.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,525 (92.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,505 (89.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e504 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep duration (hour)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.00 [6.00; 8.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.00 [6.00; 8.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.00 [6.00; 8.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.00 [6.00; 8.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.00 [6.00; 8.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,529 (70.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,290 (83.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,162 (73.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,089 (68.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e988 (58.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,071 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e477 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e568 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e707 (41.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum cotinine ((ng/mL))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05 [0.02; 34.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 [0.01; 3.39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05 [0.01; 30.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05 [0.02; 62.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07 [0.02; 65.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoverty-to-income ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40 [1.75; 5.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.69 [1.94; 5.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.49 [1.83; 5.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.25 [1.64; 5.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15 [1.57; 5.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaffeine intake (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.50 [46.0; 242.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.26 [33.94; 233.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.03 [49.00; 254.82]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.0 [48.00; 242.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.47 [52.23; 247.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.00 [41.00; 45.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.00 [41.00; 45.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.00 [41.00; 45.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.00 [41.00; 45.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.00 [40.00; 45.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlanine aminotransferase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.00 [17.00; 29.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0 [15.0; 23.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0 [16.00; 26.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.0 [18.00; 30.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.0 [20.00; 36.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspartate aminotransferase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.00 [20.00; 27.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.00 [19.00; 26.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00 [19.00; 26.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.0 [19.00; 28.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0 [21.00; 29.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUric acid (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.50 [4.60; 6.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.70 [4.00; 5.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.30 [4.50; 6.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.60 [4.80; 6.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.20 [5.40; 7.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.02 [65.42; 88.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.49 [62.76; 83.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.02 [65.42; 88.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.91 [67.18; 88.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.56 [68.95; 90.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol consumption (drinks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00 [1.00; 3.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00 [1.00; 3.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00 [1.00; 3.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00 [1.00; 3.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00 [1.00; 3.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-cholesterol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.92 [2.35; 3.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.59 [2.12; 3.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90 [2.38; 3.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.08 [2.53; 3.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.08 [2.43; 3.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Association between the CMI and sleep quality\u003c/h2\u003e\n \u003cp\u003eThe logistic regression model for the relationship between CMI and sleep health was shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The CMI was statistically positively associated with sleep trouble in all models and all the P for trend was less than 0.05. Compared to the Q1 group, the risk of sleep trouble increased in the Q2, Q3, and Q4 groups by 7%, 22%, and 49 in the Model 3, respectively. The CMI was positively associated with sleep disorder (P less than 0.05). However, there was no significant difference according to categorical variables from the Q1 group to the Q4 group in Model 3. In addition, CMI showed a negative association with sleep duration in Model 1 and Model 2, but there was no relationship between CMI and sleep duration in Model 3.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe relationship between CMI and sleep quality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep trouble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (1.21 to 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43 (1.29 to 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (1.19 to 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (0.90 to 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15 (0.93 to 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 (0.85 to 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.5427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24 (1.00 to 1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30 (1.05 to 1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (0.94 to 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50 (1.22 to 1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64 (1.33 to 2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49 (1.14 to 1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.0042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.0028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSleep disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.72 (1.49 to 1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66 (1.42 to 1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (1.0002 to 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.0498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43 (0.96 to 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (0.91 to 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (0.72 to 1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.7190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61 (1.05 to 2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48 (0.95 to 2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.58 to 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.8677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.37 (1.63 to 3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11 (1.44 to 3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 (0.70 to 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.7408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.8547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMD (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMD (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMD (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07 (-0.14 to 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06 (-0.14 to 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0003 (-0.11 to 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08 (-0.18 to 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08 (-0.18 to 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04 (-0.14 to 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.4739\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13 (-0.24 to -0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14 (-0.24 to -0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04 (-0.16 to 0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.5343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12 (-0.23 to -0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12 (-0.24 to -0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0003 (-0.11,0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eOR, odds ratio; MD, mean difference; CI, confidence intervals. Q1: the lowest CMI group. Q2: the lower CMI group. Q3: the higher CMI group. Q4: the highest CMI group.\u003c/p\u003e\n \u003cp\u003eThe model 1 was not adjusted for any covariates.\u003c/p\u003e\n \u003cp\u003eThe model 2 was adjusted by age and gender.\u003c/p\u003e\n \u003cp\u003eThe model 3 was adjusted by age, gender, race/ethnicity, educational background, PIR, serum cotinine, alcohol consumption, BMI, Caffeine intake, hypertension, LDL-cholesterol, albumin, alanine aminotransferase, aspartate aminotransferase, uric acid, and creatinine.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Subgroup analyses\u003c/h2\u003e\n \u003cp\u003eIn subgroup analyses of CMI with sleep trouble and sleep disorder (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), statistically significant interactions were marked between CMI and sleep trouble in the gender and hypertension subgroup (P for interaction was 0.0005 and 0.0003, respectively). Similar results were found in CMI and sleep disorder (P for interaction was 0.0236 and 0.0049, respectively). No significant interactions were observed in the subgroups stratified by educational background, age, or race/ethnicity. However, a positive association between CMI and sleep trouble was identified in non-Hispanic White and non-Hispanic Black populations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Additional analysis\u003c/h2\u003e\n \u003cp\u003eTo explore the associations between CMI parameters and sleep health outcomes, we analyzed the relationships with sleep trouble, sleep disorders, and sleep duration independently (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The TG was positively associated with sleep trouble (OR,1.23; 95%CI, 1.12 to 1.35). In addition, WC was significantly positively associated with sleep trouble (OR,1.02; 95%CI, 1.01 to 1.03) and sleep disorder (OR,1.03; 95%CI, 1.02 to 1.04). However, no association was found between HDL-C or height and sleep health.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation of sleep quality with parameters in CMI\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSleep trouble (OR, 95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSleep disorder (OR, 95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSleep duration (MD, 95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (1.12 to 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (0.90 to 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02 (-0.04 to 0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.5112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.65 to 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.68 to 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07 (-0.04 to 0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.1920\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (1.01 to 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (1.02 to 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0001 (-0.0042 to 0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.9618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01(1.00 to 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.98 to 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0043 (-0.0004 to 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.0719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eTG, triglyceride; HDL-C, high-density lipoprotein cholesterol; WC, waist circumference; OR, odds ratio; CI, confidence intervals;\u003c/p\u003e\n \u003cp\u003eMD, mean difference. The model was adjusted by age, gender, race/ethnicity, educational background, PIR, serum cotinine, alcohol consumption, BMI, caffeine intake, hypertension, LDL-cholesterol, albumin, alanine aminotransferase, aspartate aminotransferase, uric acid, and creatinine.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eA population-based cohort study revealed a significant association between CMI levels and sleep health, particularly in relation to sleep trouble. Even after adjusting for potential confounding variables, including gender, age, education level, coffee consumption, and laboratory test results, CMI remained positively correlated with sleep trouble. This association was especially pronounced in male and hypertensive populations.\u003c/p\u003e\u003cp\u003eThe CMI was considered an indicator of central obesity. Previous studies found that excessive daytime sleepiness (EDS) in pilots leads to an association with reduced performance and fatigue, centrally obese (based on WC) pilots have a high incidence of EDS (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Studies on the relationship between sleep duration and different obesity indicators showed a negative association between sleep duration and obesity-related indicators (BMI, waist circumference, etc.) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). School-aged children showed that later bedtimes on non-school days may be associated with increased Waist Height Ratio (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). A study of the sleep status of Saudi adults showed that higher TG levels were associated with poorer sleep health (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). However, few studies combined waist-to-height ratio with lipid indices to reveal the relationship between CMI and sleep health. Our study demonstrated that CMI was positively associated with sleep trouble as well as sleep disorder, however, there were no significant differences between CMI with sleep duration in the adjusted model. Similar to previous studies that reported no relationship between obesity and sleep duration (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). CMI as an indicator involving obesity, our results were similar to previous studies. Although the underlying molecular mechanism of action between CMI and sleep health is not clear, some explanations could be derived from previous relevant studies. A larger CMI means a greater tendency towards centripetal obesity. Previous studies have shown that obesity means higher levels of oxidation and inflammation in the body (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Notably, antioxidant status has been positively correlated with sleep quality in U.S. populations (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). while chronic inflammation, which is prevalent in obese individuals, has been shown to adversely affect sleep health (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Furthermore, obesity-induced alterations in gut microbiota composition and function may represent a critical therapeutic target (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). as interventions targeting gut dysbiosis and intestinal inflammation show promise for improving sleep disorders (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These interconnected physiological mechanisms may collectively mediate the association between cardiometabolic index and sleep health outcomes.\u003c/p\u003e\u003cp\u003eSubgroup analyses based on gender, age, education level, hypertension status, and race/ethnicity indicated that the association between CMI and sleep trouble, including sleep disorders, varied significantly by gender and hypertension status. CMI would be higher in older men than in younger. Overall, males have a higher CMI than females, while female's CMI is increasing with age (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). A Japanese study showed that the TG/HDL-C ratio and CMI increased with increasing white blood cell counts in Japanese males (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Among Japanese men with diabetes, mild-to-moderate drinking participants had lower CMI than non-drinking participants, possibly due to the positive association between alcohol consumption and HDL-C (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In contrast, an analysis of NHANES showed gender differences in blood lipids and leukocytes, with increased HDL-C appearing to correlate negatively with leukocyte counts in males, and lowering blood lipids might have anti-inflammatory and immunomodulatory effects (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The effect of sex hormone levels on sleep should not be ignored. Females were worse than males at subjective sleep but performed better than males when tested using polysomnography, sex hormones appeared to operate in the area of the brain that controls sleep (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In addition, sleep quality among non-Hispanic Black individuals was poorer compared to Hispanic individuals, potentially due to various social stressors (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Blacks have the highest rate of obesity in the U.S. population (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). perceptions of neighborhood environments may significantly influence sleep health in certain racial groups, such as Black and Hispanic populations (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). These findings suggest that race-specific sleep interventions may be both feasible and necessary. In subgroup analyses, the relationship between CMI and sleep disorder was statistically significant in both males and females, whereas the relationship between CMI and sleep disorder was only present in males. The diagnosis of sleep disorders was obtained from physicians, and our results were consistent with those of previous studies. Attention should be paid to the performance of males in sleep health.\u003c/p\u003e\u003cp\u003eThe importance of CMI in hypertensive populations has also been validated in other studies. In hypertensive patients, CMI level is positively associated with the risk of new cardiovascular disease (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). CMI is shown to correlate with the degree of atherosclerosis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Atherosclerosis is also exacerbated by short sleep and poor sleep health (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Alzheimer's disease (AD) is often associated with comorbid hypertension and AD progression is often followed by sleep disorders (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). A community-based cohort study suggesting a non-linear relationship between sleep quality and risk of hypertension was confirmed (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). A meta-analysis similarly showed that short objective sleep duration can affect cardiovascular health and is associated with a higher risk of hypertension (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Our results showed that the association between CMI and sleep disorder was statistically significant in the hypertensive population. Individuals with a higher CMI might have sleep trouble that are comorbid with hypertension. It suggested that we should pay attention to CMI and sleep health in the hypertensive population from the perspective of prevention of cardiovascular and cerebrovascular complications in our priority population. It was feasible to develop interventions for CMI and sleep health. First, on a psychological level, we should encourage participants with sleep problems that having sleep health was possible. Second, enhancing physical activity to reduce WC was a feasible approach according to the CMI calculation (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), especially among males diagnosed with hypertension. Third, lowering TG levels with safe medication was also a potential measure.\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be acknowledged. First, the assessment of sleep health and hypertension relied on subjective questionnaire data rather than objective machine-based measurements, although the modified Pittsburgh Sleep Quality Index provided relatively robust sleep health evaluation. Second, potential fluctuations in covariates, particularly the use of sleep medications, may have influenced our results. Third, the cross-sectional design limits our ability to establish causal relationships between the studied variables. Nevertheless, this study possesses notable strengths. The large sample size enhances the generalizability of findings to the U.S. population. Additionally, we systematically controlled for numerous potential confounding variables and performed comprehensive subgroup analyses to identify key population characteristics. Future prospective studies are warranted to validate the potential utility of CMI as an indicator for population-level sleep health improvement.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe study revealed a significant association between CMI levels and sleep health indicators, offering novel insights for sleep health enhancement strategies. Targeted interventions focusing on CMI modulation, such as pharmacological treatments or exercise regimens aimed at lipid profile optimization and waist circumference reduction, may be potentially effective for population-level sleep health improvement. Furthermore, comprehensive prospective studies are warranted to elucidate the underlying mechanisms of lipid metabolism's impact on sleep regulation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Center for Health Statistics institutional review board approved the program and all participants in NHANES had written informed consent (Protocol Number: Protocol #2005-06; Protocol #2011-17). The studies were conducted in accordance with the local legislation and institutional requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES (https://www.cdc.gov/nchs/nhanes/index.htm) provided the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChenpeng Zheng: Visualization, Data curation, Software, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Chaote Zhao: Data curation, Software, Visualization, Writing \u0026ndash; original draft. \u0026nbsp;Ran Zhang: Data curation, Software, Writing \u0026ndash; original draft. Xiong Lei: Supervision, Methodology, Conceptualization, Software, Formal analysis, Data curation, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe were grateful to all the participants and staff involved in the NHANES program.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDeng MG, Nie JQ, Li YY, Yu X, Zhang ZJ. Higher HEI-2015 Scores Are Associated with Lower Risk of Sleep Disorder: Results from a Nationally Representative Survey of United States Adults. Nutrients. 2022;14(4).\u003c/li\u003e\n\u003cli\u003eL\u0026eacute;ger D, Bayon V. Societal costs of insomnia. Sleep Med Rev. 2010;14(6):379-89.\u003c/li\u003e\n\u003cli\u003eZhao M, Tuo H, Wang S, Zhao L. 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Ethn Health. 2024:1-19.\u003c/li\u003e\n\u003cli\u003eCai X, Hu J, Wen W, Wang J, Wang M, Liu S, et al. Associations of the Cardiometabolic Index with the Risk of Cardiovascular Disease in Patients with Hypertension and Obstructive Sleep Apnea: Results of a Longitudinal Cohort Study. Oxid Med Cell Longev. 2022;2022:4914791.\u003c/li\u003e\n\u003cli\u003eFan B, Tang T, Zheng X, Ding H, Guo P, Ma H, et al. Sleep disturbance exacerbates atherosclerosis in type 2 diabetes mellitus. Front Cardiovasc Med. 2023;10:1267539.\u003c/li\u003e\n\u003cli\u003eMaciejewska K, Czarnecka K, Szymański P. A review of the mechanisms underlying selected comorbidities in Alzheimer\u0026apos;s disease. Pharmacol Rep. 2021;73(6):1565-81.\u003c/li\u003e\n\u003cli\u003eChen C, Zhang B, Huang J. Objective sleep characteristics and hypertension: a community-based cohort study. Front Cardiovasc Med. 2024;11:1336613.\u003c/li\u003e\n\u003cli\u003eDai Y, Vgontzas AN, Chen L, Zheng D, Chen B, Fernandez-Mendoza J, et al. A meta-analysis of the association between insomnia with objective short sleep duration and risk of hypertension. Sleep Med Rev. 2024;75:101914.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"eating-and-weight-disorders-studies-on-anorexia-bulimia-and-obesity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eawd","sideBox":"Learn more about [Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity](https://www.springer.com/journal/40519)","snPcode":"40519","submissionUrl":"https://submission.nature.com/new-submission/40519/3","title":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cardiometabolic index, Sleep health, NHANES, Obesity, Cross-sectional studies","lastPublishedDoi":"10.21203/rs.3.rs-7079101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7079101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe cardiometabolic index (CMI) is a novel indicator of central obesity. This study aimed to investigate the association between CMI and sleep health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eUsing data from the National Health and Nutrition Examination Survey (NHANES), we calculated CMI values and employed univariate and multivariate logistic regression analyses to determine whether CMI is an independent risk factor for sleep health. CMI was categorized into quartiles (Q1 to Q4). Subgroup analyses were conducted, and interaction P-values were calculated to identify high-risk populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 6,600 participants were included in the study. The prevalence of sleep disturbances was 22.2% (n = 1,589), and 7.6% (n = 504) of participants reported sleep disorders. Higher CMI levels were significantly associated with poor sleep health. Specifically, CMI was independently associated with an increased risk of sleep disturbances (OR: 1.35; 95% CI: 1.19-1.54) and sleep disorders (OR: 1.22; 95% CI: 1.0002-1.50). Compared to the Q1 group, the risk of sleep disturbances increased by 49% in the Q4 group. Subgroup analyses revealed statistically significant interactions between CMI and sleep disturbances or sleep disorders in males and individuals with hypertension (all P for interaction \u0026lt; 0.05). These findings highlight the need for increased attention to this association, particularly among males and hypertensive populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The findings suggested that CMI might be independently associated with sleep health, particularly sleep disturbances. Interventions targeting CMI could potentially improve sleep health outcomes.\u003c/p\u003e\n\u003cp\u003e“Level of Evidence: Level II, controlled trial without randomization”\u003c/p\u003e","manuscriptTitle":"Association between the cardiometabolic index and sleep health in the United States: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 05:33:06","doi":"10.21203/rs.3.rs-7079101/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-23T21:09:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241480046025866325088915858991227191121","date":"2026-01-07T21:40:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-01T15:43:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-12T14:36:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-11T01:54:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","date":"2025-07-09T02:51:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"eating-and-weight-disorders-studies-on-anorexia-bulimia-and-obesity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eawd","sideBox":"Learn more about [Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity](https://www.springer.com/journal/40519)","snPcode":"40519","submissionUrl":"https://submission.nature.com/new-submission/40519/3","title":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c3a8c7f2-b2fe-4074-9096-63e421e64dc8","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-13T05:33:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 05:33:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7079101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7079101","identity":"rs-7079101","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}